[HTML][HTML] Reinforcement learning for electric vehicle applications in power systems: A critical review

D Qiu, Y Wang, W Hua, G Strbac - Renewable and Sustainable Energy …, 2023 - Elsevier
Electric vehicles (EVs) are playing an important role in power systems due to their significant
mobility and flexibility features. Nowadays, the increasing penetration of renewable energy …

[HTML][HTML] Leveraging machine learning for efficient EV integration as mobile battery energy storage systems: Exploring strategic frameworks and incentives

MJ Salehpour, MJ Hossain - Journal of Energy Storage, 2024 - Elsevier
The emergence of electric vehicles is resha** the energy landscape, requiring the
development of innovative energy integration mechanisms to engage prosumers. However …

A coordinated active and reactive power optimization approach for multi-microgrids connected to distribution networks with multi-actor-attention-critic deep …

L Dong, H Lin, J Qiao, T Zhang, S Zhang, T Pu - Applied Energy, 2024 - Elsevier
As a promising approach to managing distributed energy, the use of microgrids has attracted
significant attention among those managing continuous connections to distribution networks …

Distributed training and distributed execution-based Stackelberg multi-agent reinforcement learning for EV charging scheduling

J Zhang, L Che, M Shahidehpour - IEEE Transactions on Smart …, 2023 - ieeexplore.ieee.org
Multi-agent deep reinforcement learning (MADRL) has been applied to EV charging
scheduling. However, it relies on centralized training and thus is significantly challenged by …

Graph learning-based voltage regulation in distribution networks with multi-microgrids

Y Wang, D Qiu, Y Wang, M Sun… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Microgrids (MGs), as localized small power systems, can effectively provide voltage
regulation services for distribution networks by integrating and managing various distributed …

[HTML][HTML] Smart distribution network operation based on energy management system considering economic-technical goals of network operator

Z Yan, Z Gao, RB Navesi, M Jadidoleslam, A Pirouzi - Energy Reports, 2023 - Elsevier
This paper expresses economic flexible–securable operation (EFSO) in smart distribution
networks (SDNs), including distributed generation and storage systems to produce green …

Meta-learning based voltage control strategy for emergency faults of active distribution networks

Y Zhao, G Zhang, W Hu, Q Huang, Z Chen, F Blaabjerg - Applied Energy, 2023 - Elsevier
With the increase of energy demand and the continuous development of renewable energy
technology, active distribution networks have become increasingly important. However, the …

[HTML][HTML] Multi-agent reinforcement learning for electric vehicle decarbonized routing and scheduling

Y Wang, D Qiu, Y He, Q Zhou, G Strbac - Energy, 2023 - Elsevier
Low-carbon transitions require joint efforts from electricity grid and transport network, where
electric vehicles (EVs) play a key role. Particularly, EVs can reduce the carbon emissions of …

[HTML][HTML] Coordinated voltage regulation of high renewable-penetrated distribution networks: An evolutionary curriculum-based deep reinforcement learning approach

T Zhang, L Yu, D Yue, C Dou, X **e, L Chen - International Journal of …, 2023 - Elsevier
With the increasing penetration of renewable energy in active distribution networks (ADNs),
voltage regulation problem is becoming more and more challenging. In this article, we focus …

[HTML][HTML] A multilayer voltage intelligent control strategy for distribution networks with v2g and power energy production-consumption units

P Fan, J Yang, S Ke, Y Wen, X Liu, L Ding… - International Journal of …, 2024 - Elsevier
The large-scale application of distributed power generation and the prospects of vehicle to
grid (V2G) technology lead to unstable operating voltage of the distribution network, but also …